End to End Learning

End-to-end learning aims to train entire systems, from raw input to final output, without manually designing intermediate steps. Current research focuses on applying this approach to diverse problems, including robotics (grasping, navigation, control), computer vision (object recognition, pose estimation, segmentation), and signal processing (communications, speech recognition), often employing neural networks, particularly recurrent and transformer architectures. This paradigm offers the potential for improved performance and generalization by optimizing all components jointly, leading to more efficient and robust systems across various scientific and engineering domains.

Papers

November 12, 2024

On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients
Satish Kumar Keshri, Nazreen Shah, Ranjitha Prasad
Catastrophic Forgetting Early Stage Convergence End to End Learning Federated Continual Learning Aggregated Gradient

November 7, 2024

Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams
Kirsten Köbschall, Lisa Hartung, Stefan Kramer
Raw Data Link Stream End to End Learning Differentiable Model Hoeffding Tree

November 5, 2024

Discovering Data Structures: Nearest Neighbor Search and Beyond
Omar Salemohamed, Laurent Charlin, Shivam Garg, Vatsal Sharan, Gregory Valiant
Nearest Neighbor End to End Learning Locality Sensitive Hashing Data Structure

September 18, 2024

GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations
Kartik Teotia, Hyeongwoo Kim, Pablo Garrido, Marc Habermann, Mohamed Elgharib, Christian Theobalt
Head Avatar End to End Learning 3D Human Head Gaussian Avatar

September 11, 2024

Combined Optimization of Dynamics and Assimilation with End-to-End Learning on Sparse Observations
Vadim Zinchenko, David S. Greenberg
Data Assimilation Dynamic Model End to End Learning Auto Regressive Simultaneous Optimization Selective Knowledge Assimilation

April 3, 2024

PoCo: Point Context Cluster for RGBD Indoor Place Recognition
Jing Liang, Zhuo Deng, Zheming Zhou, Omid Ghasemalizadeh, Dinesh Manocha, Min Sun, Cheng-Hao Kuo, Arnie Sen
Place Recognition Global Descriptor End to End Learning Point Cluster

March 21, 2024

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization
Daniel Mayfrank, Na Young Ahn, Alexander Mitsos, Manuel Dahmen
Optimization Purpose Surrogate Model Differentiable Simulation End to End Learning Mechanistic Model Data Driven Surrogate Standard Reinforcement Learning Surrogate Dynamic

March 18, 2024

What is Wrong with End-to-End Learning for Phase Retrieval?
Wenjie Zhang, Yuxiang Wan, Zhong Zhuang, Ju Sun
Phase Retrieval End to End Learning Forward Model Symmetry Breaking Nonlinear Inverse Problem

February 12, 2024

End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty
My H Dinh, James Kotary, Ferdinando Fioretto
High Uncertainty Anticipation Robust Optimization Constrained Optimization End to End Learning Parametric Optimization

January 16, 2024

OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion
Yizhuo Wu, Gagan Deep Singh, Mohammadreza Beikmirza, Leo C. N. de Vreede, Morteza Alavi, Chang Gao
End to End Learning Benchmark Framework Power Amplifier Digital Pre Distortion

October 23, 2023

End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic
Jatan Shrestha, Simon Idoko, Basant Sharma, Arun Kumar Singh
Autonomous Driving Autonomous Vehicle Trajectory Optimization Problem End to End Learning Planning Pipeline Dense Traffic Differentiable Trajectory

September 21, 2023

A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression
Moonjung Eo, Suhyun Kang, Wonjong Rhee
Low Rank Decomposition End to End Learning Differentiable Framework Structured Compression

September 3, 2023

End-to-End Learning on Multimodal Knowledge Graphs
W. X. Wilcke, P. Bloem, V. de Boer, R. H. van t Veer
Knowledge Graph Multimodal Information Multimodal Feature End to End Model End to End Learning Multimodal Knowledge Graph

June 7, 2023

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Yves Rychener, Daniel Kuhn, Tobias Sutter
End to End Stochastic Optimization Bayesian Perspective Posterior Predictive End to End Learning Robust Optimization Problem

May 14, 2023

Is end-to-end learning enough for fitness activity recognition?
Antoine Mercier, Guillaume Berger, Sunny Panchal, Florian Letsch, Cornelius Boehm, Nahua Kang, Ingo Bax, Roland Memisevic
Pose Estimation Video Dataset End to End Learning Object Centric Action

April 28, 2023

Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh, Michael Moeller, Margret Keuper, Andreas Kolb
End to End Learning Camera Parameter Optimal Sensor Pixel Layout

April 14, 2023

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting
Rushil Vohra, Ali Rajaei, Jochen L. Cremer
Multi Modality Renewable Energy Optimal Power Flow End to End Learning Renewable Generation Renewable Energy Source Convection Nowcasting

March 21, 2023

March 19, 2023

Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths
Ming Xu, Sourav Garg, Michael Milford, Stephen Gould
Dynamic Time Warping Temporal Alignment End to End Learning Better Alignment Warping Method Declarative Statement Deep Declarative

End to End Learning

Papers

On the Convergence of Continual Federated Learning Using Incrementally Aggregated Gradients

Soft Hoeffding Tree: A Transparent and Differentiable Model on Data Streams

Discovering Data Structures: Nearest Neighbor Search and Beyond

GaussianHeads: End-to-End Learning of Drivable Gaussian Head Avatars from Coarse-to-fine Representations

Combined Optimization of Dynamics and Assimilation with End-to-End Learning on Sparse Observations

PoCo: Point Context Cluster for RGBD Indoor Place Recognition

Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization

What is Wrong with End-to-End Learning for Phase Retrieval?

End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty

OpenDPD: An Open-Source End-to-End Learning & Benchmarking Framework for Wideband Power Amplifier Modeling and Digital Pre-Distortion

End-to-End Learning of Behavioural Inputs for Autonomous Driving in Dense Traffic

A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression

End-to-End Learning on Multimodal Knowledge Graphs

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Is end-to-end learning enough for fitness activity recognition?

Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters

End-to-End Learning with Multiple Modalities for System-Optimised Renewables Nowcasting

Infrastructure-based End-to-End Learning and Prevention of Driver Failure

Linear-Covariance Loss for End-to-End Learning of 6D Pose Estimation

Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths